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huggingface/CADS-dataset

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography Overview CADS is a robust, fully automated framework for segmenting 167 anatomical structures in Computed Tomography (CT), spanning from head to knee regions across diverse anatomical systems. The framework consists of two main components: CADS-dataset: 22,022 CT volumes with complete annotations for 167 anatomical structures. Most extensive whole-body CT… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/CADS-dataset.

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Dataset Card

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

<img src="https://raw.githubusercontent.com/murong-xu/CADS/refs/heads/main/resources/images/whole-body-parts-visualization.png" width="90%">

Overview

CADS is a robust, fully automated framework for segmenting 167 anatomical structures in Computed Tomography (CT), spanning from head to knee regions across diverse anatomical systems.

The framework consists of two main components:

  1. 1.CADS-dataset:
  2. 2.22,022 CT volumes with complete annotations for 167 anatomical structures.
  3. 3.Most extensive whole-body CT dataset, exceeding current collections in both scale (18x more CT scans) and anatomical coverage (60% more distinct targets).
  4. 4.Data collected from publicly available datasets and private hospital data, spanning 100+ imaging centers across 16 countries.
  5. 5.Diverse coverage of clinical variability, protocols, and pathological conditions.
  6. 6.Built through an automated pipeline with pseudo-labeling and unsupervised quality control.
  1. 1.CADS-model:
  2. 2.An open-source model suite for automated whole-body segmentation.
  3. 3.Performance validated on both public challenges and real-world hospital cohorts.
  4. 4.Available as Python script run (this GitHub repo) for flexible command-line usage.
  5. 5.Also available as a user-friendly 3D Slicer plugin with UI interface, simple installation and one-click inference.

<div style="background-color:#fffae6; padding:10px; border-radius:5px;"> This repository hosts the <strong>CADS-dataset</strong>, providing both original <strong>CT images</strong> and corresponding <strong>segmentation masks</strong> in their native spacing formats. </div>

For more information on the dataset (data collection, labeling procedures, and model derivatives etc.), please refer to the CADS paper preprint.

Useful Links

<div style="background-color:#fffae6; padding:10px; border-radius:5px;"> <b>Update (2025-10-04):</b> Fixed missing images and corrected affine/intensity errors in datasets <code>0010verse</code>, <code>0041ctrate</code>, and <code>0043newct_tri</code>, see <a href="https://huggingface.co/datasets/mrmrx/CADS-dataset/discussions/2">details for affected IDs</a>. </div>

Format

All images and segmentations are provided in NIfTI format, organized by data source.

The directory structure is as follows:

plaintext
root/
β”œβ”€β”€ dataset_name/
β”‚   β”œβ”€β”€ images/         # Original CT volumes
β”‚   β”œβ”€β”€ segmentations/  # Segmentation masks (indexing see [model labelmap](https://github.com/murong-xu/CADS/blob/main/resources/info/labelmap.md))
β”‚   └── README.md       # Dataset license, citation, and further details

Important Notice

  • β€”We are not the original owners of the CT images, except for the BrainCT-1mm and CT-TRI datasets newly released in this project.
  • β€”Users should review the corresponding README.md file in each dataset subdirectory before using the data and decide whether to include or exclude that dataset based on their intended use.

Dataset Sources Overview

The CADS-dataset comprises multiple publicly available and private-source datasets, each released under its own license.

The table below summarizes all included sources:

Directory NameDataset NameLicenseNumber of CT VolumesDetails
0001visceralgcVISCERAL Gold CorpusCustomized license40readme
0002visceralscVISCERAL Silver CorpusCustomized license127readme
0003_kits21The Kidney and Kidney Tumor Segmentation Challenge (KiTS21οΌ‰CC BY-NC-SA 4.0300readme
0004_litsLiver Tumor Segmentation Benchmark (LiTS)CC BY-NC-SA 4.0201readme
0005bcvabdomenMICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Abdomen)CC BY 4.050readme
0006bcvcervixMICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Cervix)CC BY 4.050readme
0007_chaosCHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge (CT Subset)CC BY-NC-SA 4.040readme
0008_ctorgCT-ORG: Multiple Organ Segmentation in CTCC BY 3.0140readme
0009_abdomenct1kAbdomenCT-1KCC BY 4.01062readme
0010_verseVerSe – Vertebrae Labelling and Segmentation BenchmarkCC BY-SA 4.0374readme
0011_exactEXACT'09 – Extraction of Airways from CTCustomized license40readme
0012cadpeCAD-PE – Computer Aided Detection for Pulmonary Embolism ChallengeCC BY 4.040readme
0013_ribfracRibFrac Challenge DatasetCC BY-NC 4.0660readme
0014_learn2regLearn2Reg – Abdomen MR-CT (TCIA Subset)CC BY 3.0 and TCIA Data Usage Policy16readme
0015_lndbLNDb – Lung Nodule DatabaseCC BY-NC-ND 4.0294readme
0016_lidcLIDC-IDRI – Lung Image Database Consortium and Image Database Resource InitiativeCC BY 3.0997readme
0017_lola11LOLA11 (LObe and Lung Analysis 2011)Customized license55readme
0018_sliver07SLIVER07 (Segmentation of the Liver 2007)Customized license30readme
0019tciactlymphnodesLymph Node CT Dataset (NIH, TCIA)CC BY 3.0174readme
0020tciacptac_ccrccCPTAC-CCRCC – Clear Cell Renal Cell CarcinomaCC BY 3.0258readme
0021tciacptac_luadCPTAC-LUAD – Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma CollectionCC BY 3.0133readme
0022tciactimagescovid19CT Images in COVID-19CC BY 4.0121readme
0023tciansclc_radiomicsNSCLC RadiogenomicsCC BY 3.0131readme
0024pancreasctPancreas-CTCC BY 3.080readme
0025pancreaticctcbctsegPancreatic CT-CBCT SegmentationCC BY 4.093readme
0026riderlung_ctRIDER Lung CTCC BY 4.059readme
0027tciatcga_kichTCGA-KICH (Kidney Chromophobe)CC BY 3.017readme
0028tciatcga_kircTCGA-KIRC (Kidney Renal Clear Cell Carcinoma)CC BY 3.0398readme
0029tciatcga_kirpTCGA-KIRP (Kidney Renal Papillary Cell Carcinoma)CC BY 3.019readme
0030tciatcga_lihcTCGA-LIHC (Liver Hepatocellular Carcinoma)CC BY 3.0242readme
0032_stoic2021STOIC (Study of Thoracic CT in COVID-19)CC BY-NC 4.02000readme
0033tcianlstNational Lung Screening Trial (NLST)CC BY 4.07172readme
0034_empireEMPIRE10 ChallengeCustomized license60readme
0037_totalsegmentatorTotalSegmentatorCC BY 4.01203readme
0038_amosAMOS (Multi-Modality Abdominal Multi-Organ Segmentation Challenge)CC BY 4.0200readme
0039hansegHaN-Seg: The head and neck organ-at-risk CT & MR segmentation datasetCC BY-NC-ND 4.042readme
0040_sarosSAROS: A dataset for whole-body region and organ segmentation in CT imagingMix of CC BY 3.0, CC BY 4.0, and CC BY-NC 3.0900readme
0041_ctrateCT-RATECC BY-NC-SA 4.03134readme
0042newbrainct_1mm(Newly Released) BrainCT-1mmCC BY 4.0484readme
0043newct_tri(Newly Released) CT-TRI (Triphasic Contrast-Enhanced Abdominal CTs)CC BY-NC-SA 4.0586readme

Citation

<img src="https://raw.githubusercontent.com/murong-xu/CADS/refs/heads/main/resources/images/logo.png" width="25%">

If you use any component of CADS (CADS-dataset, its curated segmentation masks, pretrained CADS-model, or the 3D Slicer extension), please cite:

bibtex
@article{xu2025cads,
  title={CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography},
  author={Xu, Murong and Amiranashvili, Tamaz and Navarro, Fernando and Fritsak, Maksym and Hamamci, Ibrahim Ethem and Shit, Suprosanna and Wittmann, Bastian and Er, Sezgin and Christ, Sebastian M. and de la Rosa, Ezequiel and Deseoe, Julian and Graf, Robert and MΓΆller, Hendrik and Sekuboyina, Anjany and Peeken, Jan C. and Becker, Sven and Baldini, Giulia and Haubold, Johannes and Nensa, Felix and Hosch, RenΓ© and Mirajkar, Nikhil and Khalid, Saad and Zachow, Stefan and Weber, Marc-AndrΓ© and Langs, Georg and Wasserthal, Jakob and Ozdemir, Mehmet Kemal and Fedorov, Andrey and Kikinis, Ron and Tanadini-Lang, Stephanie and Kirschke, Jan S. and Combs, Stephanie E. and Menze, Bjoern},
  journal={arXiv preprint arXiv:2507.22953},
  year={2025}
}
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